Dirk Ormoneit

Probabilistic & Bayesian Methods · 1995–2004

A researcher in the Science corpus: 4 papers; trusts 20 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Probabilistic & Bayesian Methods — 8 citations into it; #1,142 of 1,525 by trust here, #1,415 by citations
• Reinforcement Learning — 8 citations into it; #1,553 of 2,242 by trust here, #1,750 by citations
• Statistics and Probability — 7 citations into it; #7,870 of 12,932 by trust here, #9,714 by citations
• Statistical Learning Theory — 6 citations into it; #807 of 1,038 by trust here, #730 by citations

Whom Dirk Ormoneit trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Richard S. Sutton
2. Martin L. Puterman
3. D. Hunter
4. Sebastian Thrun
5. Jing Peng
6. Ryuichi Oka
7. Christopher J. C. H. Watkins
8. I. Baird
9. William D. Smart
10. Christopher G. Atkeson

Who trusts Dirk Ormoneit overall

1. Christiane Lemieux
2. Nicholas K. Jong
3. Jo Bovy
4. Simon Lacoste-Julien
5. Krzysztof Marcin Choromanski
6. Peter Stone
7. Benigno Uria
8. Huan Xu
9. Csaba Szepesvari
10. David W. Hogg

Papers from Dirk Ormoneit's perspective

Ordered by how strongly the researchers Dirk Ormoneit trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Gradient-based learning applied to document recognition
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. ImageNet: A large-scale hierarchical image database
7. Reinforcement Learning: An Introduction
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• Lattice Particle Filters (2001) — 26 citations
• Hoeffding's inequality for uniformly ergodic Markov chains (2002) — 162 citations
• Kernel-Based Reinforcement Learning (2004) — 139 citations
• Improved Gaussian Mixture Density Estimates Using Bayesian Penalty Terms and Network Averaging (1995)